Electroconvulsive Therapy for Hospitalized Patients with Depression
Bibliographic record
Abstract
Electroconvulsive therapy (ECT), the most effective treatment for depression in psychiatry, is underused due to stigma and lack of information regarding its risks and benefits. The aims of this thesis were to inform evidence-based use of ECT by identifying patient-level factors that influence the use of ECT and determining the association between ECT and rare but clinically important medical and psychiatric outcomes. The thesis included three projects using administrative health data of hospitalized patients with depression in Ontario, Canada between 2007 and 2017. The first project was a comprehensive assessment of patient characteristics associated with receipt of inpatient ECT in Ontario. Both clinically appropriate, and potentially inappropriate factors were associated with receipt of inpatient ECT, suggesting opportunities to increase appropriate utilization. In the second project, the risk of serious medical events associated with ECT compared to no ECT was evaluated using propensity score matching that adjusted for a large set of sociodemographic and clinical variables. There was no clinically significant increased risk of serious medical events – as measured by medical hospitalization or death – with ECT exposure. In the third project, weighting by the odds of the propensity score was used to compare the risk of suicide among individuals receiving ECT vs. similar individuals not receiving ECT in the year following discharge from psychiatric hospitalization. ECT was associated with a significantly reduced risk of suicide and all-cause death. Taken together, these findings provide important data that can be used to support the evidence-based use of ECT in a highly vulnerable clinical population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".